Thanks Robin and Sean, I will experiment with both the approaches and update you.
Thanks Pradeep On Fri, Jul 9, 2010 at 9:59 PM, Sean Owen <[email protected]> wrote: > Either approach could work. In essence they are doing something > similar. What works best for your problem will depend on the exact > data. > > On Sat, Jul 10, 2010 at 12:37 AM, Pradeep Pujari <[email protected]> > wrote: > > Hi Ted, > > > > I want to build a prototype for "people who view this item also viewd > these > > other items" > > using Mahout. I am exploring how Mahout could help. I have data like > > user_id --> item_id--->no_of_clicks. Looks to me this is not a > collaborative > > filtering problem. > > Because, this is neither finding users having similar taste not > similarilty > > between items. > > I think this is a problem of Co-occurrence discovery and can be solved by > > Association Rules Mining > > algorithms like FP Growth. Any comment on this is highly appriciated. > > > > Thanks in advance. > > Pradeep > > > > > > On Thu, Jul 8, 2010 at 5:15 PM, Ted Dunning <[email protected]> > wrote: > > > >> The answer to your first question is "yes". > >> > >> The answer to your second question (please advise) is "heh?" > >> > >> Can you explain what you are asking in a bit more detail? > >> > >> On Thu, Jul 8, 2010 at 4:57 PM, Pradeep Pujari <[email protected]> > wrote: > >> > >> > > >> > Recommendation Algorithms: Can it be used for a case like, people who > >> > viewed > >> > this item also viewed these other items? I read the taste > recommendation > >> > framework which talks about collaborative filtering. Looks to me this > >> above > >> > use case is not a collaborative filtering subject. We know the click > data > >> > and math lib can able to help. Please advise. > >> > > >> > > >> > > >
